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A coding agent stopping is not the same as the task being complete. Treat an idle or “done” signal as evidence that the run has ended or the agent believes it finished—not proof that the requested result is correct. Check for blockers, inspect the output, and verify it against the original requirements before relying on it.
What does “finished” mean?
There are two different questions: has the agent stopped working, and has it delivered what you asked for? A platform can answer the first with a run-state event. The second requires checking the task and its result.
For example, GitHub’s Copilot SDK documentation says session.idle is emitted when the tool-use loop ends. It means the agent stopped processing and is ready for another message; it does not mean the requested work is correct or complete. GitHub calls it a reliable signal that the loop ended, not a semantic verdict that the model thinks it is done. GitHub’s Copilot SDK documentation describes this behavior.
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How to interpret common completion signals
| Signal | What it tells you | What it does not establish |
|---|---|---|
Copilot SDK session.idle |
The tool loop ended and the agent is ready for another message. | That the requested task is complete or correct. |
Copilot SDK session.task_complete |
The model explicitly considers the overall task fulfilled. It may include a summary and is persisted in the event log. | That the claim is true. The signal is optional and best-effort. |
| GitHub cloud-agent task record | Task state, associated sessions, timestamps, and artifacts can be inspected. | That the API is a permanent contract: its endpoints are public preview and may change. |
| OpenAI Agents API progress events or webhooks | An application can receive progress and learn when an agent finishes or needs input. | That a code change passes a universal correctness test. |
These signals are product-specific, not interchangeable labels shared by every coding agent. GitHub’s Copilot SDK documentation says session.task_complete requires an explicit model signal and may be absent in interactive use, including when the exchange is ordinary Q&A or interrupted. An absent signal alone therefore does not prove failure. Conversely, a task-complete signal reports the model’s assessment, not independently verified success. GitHub’s SDK event documentation explains the distinction.
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OpenAI’s Agents API overview describes streaming and webhooks for learning when an agent finishes or needs input. Its overview does not define a universal test for code correctness.
How to verify a coding-agent run
- Confirm the run reached a terminal state. Use the platform’s documented status or event to establish that the run stopped. In the Copilot SDK,
session.idleconfirms the loop ended; it is not a success verdict. - Check for blockers. Look for errors, permission requests, unanswered questions, or a status indicating that input is needed. A quiet or inactive agent may be waiting rather than finished. The exact status vocabulary depends on the product.
- Read the agent’s completion claim and summary. Treat these as the agent’s account of what it believes it accomplished. A Copilot SDK
session.task_completesignal is optional and expresses the model’s view that the task is fulfilled. - Inspect the actual result. Review the code diff, changed files, pull request, or other artifact the platform exposes. GitHub’s cloud-agent API documents task records with associated session and artifact data. Its endpoints are currently labeled public preview and subject to change. GitHub’s cloud-agent API documentation describes the available records.
- Match the result to the request. For each acceptance criterion, identify evidence in the artifact that it was met. A summary that says “done” is not a substitute for checking the requested outcomes.
- Run relevant checks. Use the project’s appropriate tests, build, linter, or manual checks. Record checks that fail or were skipped. Passing tests increase confidence, but cannot establish requirements the tests do not cover.
- Report what remains unverified. If requirements are incomplete, an error occurred, or behavior was not checked, say the run ended but the work is not verified complete.
This is a practical verification method, not a vendor-certified guarantee. The documentation describes run states and available artifacts; it does not validate one checklist as sufficient for every project.
Can you leave a coding agent unattended?
You can let a run proceed unattended when your workflow supports monitoring and recovery, but do not treat inactivity as permission to trust the result. Configure the surrounding process to surface errors and requests for input, and arrange to inspect the output before merging or relying on it. GitHub’s Copilot SDK documentation recommends handling errors and timeouts around idle events; its autopilot description says not to mark a task complete while open questions, errors, or remaining steps exist.
For a long-running or parallel workflow, keep enough status and artifact information to identify which task stopped, what it produced, and whether it needs a person to act. GitHub’s cloud-agent task records and OpenAI’s progress events illustrate ways particular platforms expose this information, but their status names and behavior should not be assumed to apply elsewhere.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What a trustworthy “done” report should contain
A useful completion report connects the request to observable evidence. It should identify what changed, which requested outcomes were addressed, which checks ran and their results, and any remaining question or unverified behavior. If the agent only stopped, or if its own completion signal is missing, report that precisely rather than upgrading a run-state event into a claim of success.
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